Abstract
The role of artificial intelligence (AI) solutions is growing in all types of organizations. AI is embraced in the hope of increased productivity, quality and satisfaction at work. Therefore, it is essential to study the factors that influence the adoption of AI. This research is conducted as a survey among knowledge workers. Previous research indicates that large organizations tend to adopt new technologies faster than smaller ones. According to our findings, this also holds for the adoption of AI. Furthermore, in organizations that have adopted AI-enabled technologies, employees keep their knowledge and skills up to date by independent and self-driven study more often than the employees of organizations with a lower degree of AI adoption. The research results also indicate that, especially in large organizations where the rate of AI adoption is high, the extent to which the employees may affect the software and hardware they use, is also high.
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References
Makridakis, S.: The forthcoming Artificial Intelligence (AI) revolution: its impact on society and firms. Futures 90, 46–60 (2017)
Shiroishi, Y., Uchiyama, K., Suzuki, N.: Society 5.0: for human security and well-being. Computer 51(7), 91–95 (2018)
Davenport, T.H.: Process management for knowledge work. In: Brocke, J., Rosemann, M. (eds.) Handbook on Business Process Management 1. IHIS, pp. 17–35. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-642-45100-3_2
Coombs, C., Hislop, D., Taneva, S.K., Barnard, S.: The strategic impacts of Intelligent Automation for knowledge and service work: an interdisciplinary review. J. Strat. Inf. Syst. 29(4), 1–30 (2020). https://doi.org/10.1016/j.jsis.2020.101600
von Richthofen, G., Ogolla, S., Send, H.: Adopting AI in the context of knowledge work: empirical insights from German organizations. Information 13(4), 199 (2022). https://doi.org/10.3390/info13040199
Frey, C.B., Garlick, R., Friedlander, G., Mcdonald, G., Wilkie, M., Lai, A.: Technology at work v2.0. CityGroup and University of Oxford (2016)
Leesakul, N., Oostveen, A.M., Eimontaite, I., Wilson, M.L., Hyde, R.: Workplace 4.0: exploring the implications of technology adoption in digital manufacturing on a sustainable workforce. Sustainability 14(6), 3311 (2022)
Pyöriä, P.: The concept of knowledge work revisited. J. Knowl. Manag. 9(3), 116–127 (2005)
Nonaka, I., Toyama, R., Konno, N.: SECI, Ba and leadership: a unified model of dynamic knowledge creation. Long Range Plan. 22, 5–34 (2000)
Benbya, H., Pachidi, S., Jarvenpaa, S.: Artificial intelligence in organizations: implications for information systems research. J. Assoc. Inf. Syst. 22(4), 1–25 (2021)
Berente, N., Gu, B., Recker, J., Santanam, R.: Managing artificial intelligence. MIS Q. 45(3), 1433–1450 (2021). https://doi.org/10.25300/MISQ/2021/16274
Camarinha-Matos, L.M., Hamideh, A.: Collaborative networks: a new scientific discipline. J. Intell. Manuf. 16(4–5), 439–452 (2005). https://doi.org/10.1007/s10845-005-1656-3
Pumplun, L., Tauchert, C., Heidt, M.: A new organizational chassis for artificial intelligence - exploring organizational readiness factors. In: 27th European Conference on Information Systems - Information Systems for a Sharing Society, ECIS 2019, pp. 1–15 (2020)
Jöhnk, J., Weißert, M., Wyrtki, K.: Ready or not, AI comes—an interview study of organizational AI readiness factors. Bus. Inf. Syst. Eng. 63(1), 5–20 (2021). https://doi.org/10.1007/s12599-020-00676-7
Kedziora, D., Leivonen, A., Piotrowicz, W., Öörni, A.: Robotic process automation (RPA) implementation drivers: evidence of selected Nordic companies. Issues Inf. Syst. 22(2), 21–40 (2021)
Barann, B., Hermann, A., Chasin, F., Becker, J.: Supporting digital transformation in small and medium-sized enterprises: a procedure model involving publicly funded support units. In: Proceedings of the 52nd Hawaii International Conference on System Sciences (2019)
Braganza, A., Chen, W., Canhoto, A., Sap, S.: Productive employment and decent work: the impact of AI adoption on psychological contracts, job engagement and employee trust. J. Bus. Res. 131, 485–494 (2021)
Cockburn, I.M., Henderson, R., Stern, S.: The impact of artificial intelligence on innovation: an exploratory analysis. In: The Economics of Artificial Intelligence: An Agenda, pp. 115–146. University of Chicago Press (2018)
Parasuraman, A.: Technology readiness index (TRI) a multiple-item scale to measure readiness to embrace new technologies. J. Serv. Res. 2(4), 307–320 (2000)
Agarwal, R., Prasad, J.: Are individual differences germane to the acceptance of new information technologies? Decis. Sci. 30(2), 361–391 (1999)
Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manag. Sci. 35(8), 982–1003 (1989). https://doi.org/10.1287/mnsc.35.8.982
Rogers, E.M.: Diffusion of Innovations. Free Press, New York (1995)
Haneem, F., Kama, N., Bakar, N.A.A.: Critical influential determinants of IT innovation adoption at organisational level in local government context. IET Softw. 13(4), 233–240 (2019)
Bughin, J., et al.: Artificial intelligence: the next digital frontier? (2017) https://apo.org.au/node/210501
Ahmad, R., Kyratsis, Y.: When the user is not the chooser: learning from stakeholder involvement in technology adoption decisions in infection control. J. Hosp. Infect. 81(3), 163–168 (2012)
Gosling, S.D., Rentfrow, P.J., Swann, W.B.: A very brief measure of the Big-Five personality domains. J. Res. Pers. 37(6), 504–528 (2003). https://doi.org/10.1016/S0092-6566(03)00046-1
Weber, E.U., Blais, A.-R., Betz, N.E.: A domain-specific risk-attitude scale: measuring risk perceptions and risk behaviors. J. Behav. Decis. Making 15(4), 263–290 (2002). https://doi.org/10.1002/bdm.414
Kauttonen, J., Suomala, J.: The decision science of voting: behavioral evidence of factors in candidate valuation. In: Proceedings of the 41st Annual Conference of the Cognitive Science Society (2019)
Bley, K., Leyh, C., Schäffer, T.: Digitization of German enterprises in the production sector - do they know how “digitized” they are? In: Proceedings of the 22nd Americas Conference on Information Systems (AMCIS 2016) (2016)
Bürkner, P.C., Vuorre, M.: Ordinal regression models in psychology: a tutorial. Adv. Methods Pract. Psychol. Sci. 2(1), 77–101 (2019)
Rennie, J.D.M., Srebro, N.: Fast maximum margin matrix factorization for collaborative prediction. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 713–719 (2005)
Vehtari, A., Gelman, A., Gabry, J.: Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27(5), 1413–1432 (2017). https://doi.org/10.1007/s11222-016-9696-4
Veale, M., Borgesius, F.Z.: Demystifying the Draft EU Artificial Intelligence Act—analysing the good, the bad, and the unclear elements of the proposed approach. Comput. Law Rev. Int. 22(4), 97–112 (2021)
Shuman, J., Twombly, J.: Collaborative networks are the organization: an innovation in organization design and management. Vikalpa 35(1), 1–14 (2010). https://doi.org/10.1177/0256090920100101
Aunimo, L., Huttunen, S.: A model for building skills and knowledge needed in the job market. In: Proceedings of the 14th International Technology, Education and Development Conference, pp. 6362–6369 (2020). https://doi.org/10.21125/inted.2020.1713
Stolze, A., Sailer, K., Gillig, H.: Entrepreneurial mindset as a driver for digital transformation - a novel educational approach from university-industry interactions. In: European Conference on Innovation and Entrepreneurship, pp. 806–813, XXI. Academic Conferences International Limited (2018)
Saghafian, M., Laumann, K., Skogstad, M.R.: Stagewise overview of issues influencing organizational technology adoption and use. Front. Psychol. 12, 630145 (2021). https://doi.org/10.3389/fpsyg.2021.630145
Acknowledgements
The research presented in this paper is supported partly by the “TT-TOY: AI comes: support, competences and collaboration into order” project funded by the Finnish Institute of Occupational Health and the “AI Driver! - Digital Business Transformation, Human AI Interaction in Service Business and Open Education” project funded by the Finnish Ministry of Education and Culture.
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Aunimo, L., Kauttonen, J., Lahtinen, A., Lagstedt, A., Alamäki, A. (2023). Factors Affecting the Adoption of AI by Organizations - From the Perspective of Knowledge Workers. In: Camarinha-Matos, L.M., Boucher, X., Ortiz, A. (eds) Collaborative Networks in Digitalization and Society 5.0. PRO-VE 2023. IFIP Advances in Information and Communication Technology, vol 688. Springer, Cham. https://doi.org/10.1007/978-3-031-42622-3_33
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